Apoorv – UNICEF Indicators Dashboard

Code 
import pandas as pd
import plotly.express as px
import plotly.graph_objects as go

import warnings
warnings.filterwarnings('ignore')

indicator1 = pd.read_csv("unicef_indicator_1.csv")
indicator2 = pd.read_csv("unicef_indicator_2.csv")
metadata = pd.read_csv("unicef_metadata.csv")
indicator1 = indicator1.dropna(subset=["obs_value"])
indicator2 = indicator2.dropna(subset=["obs_value"])
metadata = metadata.dropna(subset=["year"])
Code 
early_marriage = indicator1[(indicator1["indicator"].str.contains("household chores", case=False)) & (indicator1["sex"] == "Female")]
early_marriage_top10 = early_marriage.sort_values("obs_value", ascending=False).head(10)
fig = px.bar(early_marriage_top10, x="country", y="obs_value", title="Top 10 Countries - household chores (Females 10-14)", text="obs_value")
fig.show()

Widespread Issue: The bar chart shows countries with over 20% of girls aged 10-14 engaged in excessive household chores, limiting their opportunities

Policy Focus: These top 10 nations highlight the urgent need for interventions to reduce chore burdens and support girls’ education in low-income regions

Code 
overweight = indicator2[indicator2["indicator"].str.contains("overweight", case=False)]
overweight = overweight.groupby("time_period")["obs_value"].mean().reset_index()
fig = px.line(overweight, x="time_period", y="obs_value", title="Trend of Overweight in Under 5 Children")
fig.show()

Gradual Rise: The line graph reveals a slight increase in global overweight prevalence among under-5 children over time.

Health Alert: This trend underscores the need for global nutrition programs to combat rising childhood obesity.

Code 
overweight_latest = indicator2[indicator2["time_period"] == indicator2["time_period"].max()]
overweight_latest = overweight_latest.merge(metadata, left_on=["country"], right_on=["country"])
overweight_latest = overweight_latest.dropna(subset=["GDP per capita (constant 2015 US$)"])
fig = px.scatter(overweight_latest, x="GDP per capita (constant 2015 US$)", y="obs_value", hover_name="country", title="Overweight % (Under 5) vs GDP per Capita")
fig.show()

Disparity Highlight: Poorer countries exhibit higher rates, suggesting economic development could reduce childhood obesity.

Code 
early_marriage = indicator1[(indicator1["indicator"].str.contains("household chores", case=False)) & (indicator1["sex"] == "Female")]
early_marriage_top10 = early_marriage.sort_values("obs_value", ascending=False).head(10)
fig = px.bar(early_marriage_top10, x="country", y="obs_value", title="Top 10 Countries - household chores (Females 10-14)", text="obs_value")
fig.show()
Code 
overweight = indicator2[indicator2["indicator"].str.contains("overweight", case=False)]
overweight = overweight.groupby("time_period")["obs_value"].mean().reset_index()
fig = px.line(overweight, x="time_period", y="obs_value", title="Trend of Overweight in Under 5 Children")
fig.show()
Code 
overweight_latest = indicator2[indicator2["time_period"] == indicator2["time_period"].max()]
overweight_latest = overweight_latest.merge(metadata, left_on=["country"], right_on=["country"])
overweight_latest = overweight_latest.dropna(subset=["GDP per capita (constant 2015 US$)"])
fig = px.scatter(overweight_latest, x="GDP per capita (constant 2015 US$)", y="obs_value", hover_name="country", title="Overweight % (Under 5) vs GDP per Capita")
fig.show()
Code 
early_marriage_latest = early_marriage[early_marriage["time_period"] == early_marriage["time_period"].max()]
early_marriage_latest = early_marriage_latest.merge(metadata, left_on=["country"], right_on=["country"])
early_marriage_latest = early_marriage_latest.dropna(subset=["Life expectancy at birth, total (years)"])
fig = px.scatter(early_marriage_latest, x="Life expectancy at birth, total (years)", y="obs_value", hover_name="country", title="household chores % vs Life Expectancy")
fig.show()

Social Impact: Higher chore rates in lower life expectancy nations indicate socioeconomic barriers that need addressing.

Code 
overw_birth = overweight_latest.dropna(subset=["Birth rate, crude (per 1,000 people)"])
fig = px.scatter(overw_birth, x="Birth rate, crude (per 1,000 people)", y="obs_value", hover_name="country", title="Birth Rate vs Overweight Children (%)")
fig.show()

Positive Correlation: Regions with higher birth rates tend to have elevated overweight percentages in under-5 children.

Regional Insight: This scatter plot suggests that high birth rates may strain resources, contributing to childhood obesity in some areas.

Code 
gdp_life = metadata.dropna(subset=["GDP per capita (constant 2015 US$)", "Life expectancy at birth, total (years)"])
fig = px.scatter(gdp_life, x="GDP per capita (constant 2015 US$)", y="Life expectancy at birth, total (years)", hover_name="country", trendline="ols", title="GDP per Capita vs Life Expectancy")
fig.show()
Code 
observations = [
    "1. Countries with higher GDP per capita generally have lower rates of child overweight under 5 years.",
    "2. Sub-Saharan African countries dominate the top 10 for household chores among girls aged 10-14, highlighting a regional challenge.",
    "3. High birth rates may contribute to poor nutritional outcomes, increasing overweight prevalence in children under 5.",
    "4. Birth rates are positively correlated with higher prevalence of overweight in children under 5 in some regions.",
    "5. Global average overweight percentage in under 5 children has increased slightly over recent years."
]
for line in observations:
    print(line)
1. Countries with higher GDP per capita generally have lower rates of child overweight under 5 years.
2. Sub-Saharan African countries dominate the top 10 for household chores among girls aged 10-14, highlighting a regional challenge.
3. High birth rates may contribute to poor nutritional outcomes, increasing overweight prevalence in children under 5.
4. Birth rates are positively correlated with higher prevalence of overweight in children under 5 in some regions.
5. Global average overweight percentage in under 5 children has increased slightly over recent years.